TITAN

TITLE OF THE PROJECT:

TITAN: Trusted envIronments for confidenTiAl compuTiNg and secure data sharing

GENERAL DATA:

Program: HORIZONTE EUROPE EU
Reference: 101129822
Date of RESOLUTION: 10/23/2023
Date of execution: 02/01/2024-31/01/2027

PRESENTATION AND OBJECTIVES:

The TITAN project aims to advance secure data exchange through a modular platform based on Trusted Execution Environments (TEEs) and Confidential Computing . It is oriented to the processing of sensitive data in areas such as healthcare and public administration, ensuring interoperability with EOSC standards.
TITAN addresses the legal, ethical and technical challenges of cross-border data sharing by integrating blockchain-based access control, data anonymization through GANs and federated data quality frameworks.
The platform enables secure artificial intelligence and machine learning (AI/ML) collaboration through federated learning and confidential multi-party computation, leveraging TEEs to train models while preserving privacy. In addition, TITAN develops tools for lifecycle management, remote attestation and confidential GPU support, enabling secure analytics in the cloud.
Trust management follows a Zero Trust architecture, using Verifiable Credentials, Decentralized Identifiers (DIDs) and Distributed Logging Technologies (DLTs) for identity management and access control. Smart contracts executed within TEEs enhance privacy and compliance with regulations such as GDPR.
TITAN also promotes privacy-preserving audits using blockchain Hyperledger and supportssemantic interoperability for search and federated data sharing. The project combines state-of-the-art cryptographic techniques, secure infrastructure and policy analytics to enable legal, scalable and reliable data collaboration across institutions and borders.
In the 36-month project, ITA collaborates with SARGA and the Veneto Region (Italy) on sharing crowdsourced data for ML-based modeling by reducing the risk of sharing. More specifically, this use case aims to use Federated Learning (FL) for the creation and improvement of phenology and pest risk prediction models in vineyards. In the implementation of the use case, FL capabilities will be used to train local models from global models to improve the performance of the former and, if the agreement between the parties so requires, the local improvements can be incorporated into the global models.

PARTICIPATING ENTITIES:

• UNIVERSITY OF MURCIA (UMU)
• FUJITSU TECHNOLOGY SOLUTIONS (LUXEMBOURG) SA (FUJ)
• PRIVREDNO DRUSTVO ZENTRIX LAB DRUSTVO SA OGRANICENOM ODGOVORNOSCU PANCEVO (ZEN)
• CANARY BIT AB (CAB)
• ULTRAVIOLET CONSULT DOO (UVC)
• F6S NETWORK IRELAND LIMITED (F6S
• ITA-SUOMEN YLIOPISTO (UEF)
• ODYSSEUS DATA SERVICES SRO (ODY)
• TRILATERAL RESEARCH LIMITED (TRI)
• CHARITE – UNIVERSITAETSMEDIZIN BERLIN (CHA)
• INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE (INS)
• SOCIEDAD ARAGONESA DE GESTION AGROAMBIENTAL SL (SAR)
• REGIONE DEL VENETO (VEN)
• UNIVERSITY OF KOBLENZ (UKO)
• INSTITUTO TECNOLOGICO DE ARAGON (ITA)
• FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV (Fraunhofer)

BUDGET:

TOTAL PROJECT BUDGET: 4,999,200.97 €
TOTAL ITA BUDGET: 173,000 €.

FINANCING:

TOTAL PROJECT GRANT: 4.999.200,97 €
TOTAL FUNDING ITA: 173.000 €.

“Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them.”

“The project No 101129822 is funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them.”

The TITAN project whose reference is 101129822 has been 100% co-financed by the EU – Horizon Europe.